Overview

Dataset statistics

Number of variables23
Number of observations3164
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory568.7 KiB
Average record size in memory184.0 B

Variable types

Categorical3
Numeric20

Alerts

Time has a high cardinality: 3136 distinct values High cardinality
CO2 at any moment (PPM) has a high cardinality: 3163 distinct values High cardinality
Illumination level (Lux) is highly correlated with Color Tempreture and 5 other fieldsHigh correlation
Color Tempreture is highly correlated with Illumination level (Lux) and 3 other fieldsHigh correlation
O2 Concentration (%Vol) is highly correlated with Illumination level (Lux) and 6 other fieldsHigh correlation
LPG concen (ppm) is highly correlated with Methan Concen (ppm)High correlation
Alcohole Concen (ppm) is highly correlated with CO concen (ppm) and 2 other fieldsHigh correlation
Methan Concen (ppm) is highly correlated with Color Tempreture and 2 other fieldsHigh correlation
CO concen (ppm) is highly correlated with Alcohole Concen (ppm) and 4 other fieldsHigh correlation
Hydrogyn Concen (ppm) is highly correlated with Illumination level (Lux) and 6 other fieldsHigh correlation
Netric tVOC level (ppm) is highly correlated with Illumination level (Lux) and 4 other fieldsHigh correlation
VOC Formerdahied Level (ppm) is highly correlated with Methan Concen (ppm) and 1 other fieldsHigh correlation
Room Temp ( C ) is highly correlated with Illumination level (Lux) and 7 other fieldsHigh correlation
Room reative humadity % is highly correlated with O2 Concentration (%Vol) and 4 other fieldsHigh correlation
Radiant Temp 1 (C) is highly correlated with Room Temp ( C ) and 2 other fieldsHigh correlation
Radiant Temp 2 (C) is highly correlated with Radiant Temp 1 (C) and 1 other fieldsHigh correlation
Radiant Temp 3 (C) is highly correlated with Radiant Temp 1 (C).1High correlation
Radiant Temp 1 (C).1 is highly correlated with Illumination level (Lux) and 7 other fieldsHigh correlation
Illumination level (Lux) is highly correlated with Color Tempreture and 6 other fieldsHigh correlation
Color Tempreture is highly correlated with Illumination level (Lux) and 4 other fieldsHigh correlation
O2 Concentration (%Vol) is highly correlated with Illumination level (Lux) and 5 other fieldsHigh correlation
LPG concen (ppm) is highly correlated with Methan Concen (ppm)High correlation
Alcohole Concen (ppm) is highly correlated with CO concen (ppm) and 1 other fieldsHigh correlation
Methan Concen (ppm) is highly correlated with LPG concen (ppm)High correlation
CO concen (ppm) is highly correlated with Illumination level (Lux) and 6 other fieldsHigh correlation
Hydrogyn Concen (ppm) is highly correlated with Illumination level (Lux) and 7 other fieldsHigh correlation
Netric tVOC level (ppm) is highly correlated with Illumination level (Lux) and 5 other fieldsHigh correlation
Different VOC (CO) Level (ppm) is highly correlated with CO concen (ppm)High correlation
Room Temp ( C ) is highly correlated with Illumination level (Lux) and 8 other fieldsHigh correlation
Room reative humadity % is highly correlated with O2 Concentration (%Vol) and 4 other fieldsHigh correlation
Radiant Temp 1 (C) is highly correlated with Room Temp ( C ) and 2 other fieldsHigh correlation
Radiant Temp 2 (C) is highly correlated with Radiant Temp 1 (C) and 1 other fieldsHigh correlation
Radiant Temp 3 (C) is highly correlated with Radiant Temp 1 (C).1High correlation
Radiant Temp 1 (C).1 is highly correlated with Illumination level (Lux) and 5 other fieldsHigh correlation
Illumination level (Lux) is highly correlated with Color TempretureHigh correlation
Color Tempreture is highly correlated with Illumination level (Lux)High correlation
CO concen (ppm) is highly correlated with Netric tVOC level (ppm) and 1 other fieldsHigh correlation
Netric tVOC level (ppm) is highly correlated with CO concen (ppm) and 1 other fieldsHigh correlation
Room reative humadity % is highly correlated with CO concen (ppm) and 1 other fieldsHigh correlation
Radiant Temp 1 (C) is highly correlated with Radiant Temp 1 (C).1High correlation
Radiant Temp 2 (C) is highly correlated with Radiant Temp 1 (C).1High correlation
Radiant Temp 3 (C) is highly correlated with Radiant Temp 1 (C).1High correlation
Radiant Temp 1 (C).1 is highly correlated with Radiant Temp 1 (C) and 2 other fieldsHigh correlation
Location is highly correlated with Illumination level (Lux) and 14 other fieldsHigh correlation
Decimal is highly correlated with Color Tempreture and 9 other fieldsHigh correlation
Illumination level (Lux) is highly correlated with Location and 8 other fieldsHigh correlation
Color Tempreture is highly correlated with Location and 15 other fieldsHigh correlation
O2 Concentration (%Vol) is highly correlated with Location and 12 other fieldsHigh correlation
LPG concen (ppm) is highly correlated with Location and 9 other fieldsHigh correlation
Alcohole Concen (ppm) is highly correlated with Location and 16 other fieldsHigh correlation
Methan Concen (ppm) is highly correlated with Decimal and 6 other fieldsHigh correlation
CO concen (ppm) is highly correlated with Location and 14 other fieldsHigh correlation
Hydrogyn Concen (ppm) is highly correlated with Location and 14 other fieldsHigh correlation
Netric tVOC level (ppm) is highly correlated with Location and 10 other fieldsHigh correlation
VOC Formerdahied Level (ppm) is highly correlated with Decimal and 6 other fieldsHigh correlation
Different VOC (CO) Level (ppm) is highly correlated with Location and 11 other fieldsHigh correlation
Room Temp ( C ) is highly correlated with Location and 15 other fieldsHigh correlation
Room reative humadity % is highly correlated with Location and 10 other fieldsHigh correlation
Radiant Temp 1 (C) is highly correlated with Location and 5 other fieldsHigh correlation
Radiant Temp 2 (C) is highly correlated with Location and 8 other fieldsHigh correlation
Radiant Temp 3 (C) is highly correlated with Alcohole Concen (ppm) and 4 other fieldsHigh correlation
Radiant Temp 1 (C).1 is highly correlated with Location and 10 other fieldsHigh correlation
Wind Speed (mm/s) is highly correlated with Location High correlation
Time is uniformly distributed Uniform
CO2 at any moment (PPM) is uniformly distributed Uniform
Illumination level (Lux) has 730 (23.1%) zeros Zeros

Reproduction

Analysis started2022-08-23 09:23:39.228071
Analysis finished2022-08-23 09:24:37.039409
Duration57.81 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Location
Categorical

HIGH CORRELATION

Distinct21
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
STU211
1720 
STU301
1303 
Corridor
 
26
CL302
 
16
AD316
 
13
Other values (16)
 
86

Length

Max length13
Median length6
Mean length6.024020228
Min length5

Characters and Unicode

Total characters19060
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowSTU301
2nd rowSTU301
3rd rowSTU301
4th rowSTU301
5th rowSTU301

Common Values

ValueCountFrequency (%)
STU2111720
54.4%
STU3011303
41.2%
Corridor 26
 
0.8%
CL30216
 
0.5%
AD31613
 
0.4%
STU302 13
 
0.4%
STU20711
 
0.3%
ArchSpace10
 
0.3%
CL3108
 
0.3%
CL3078
 
0.3%
Other values (11)36
 
1.1%

Length

2022-08-23T12:24:37.128430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stu2111720
54.4%
stu3011303
41.2%
corridor26
 
0.8%
cl30216
 
0.5%
ad31613
 
0.4%
stu30213
 
0.4%
stu20711
 
0.3%
archspace10
 
0.3%
cl3108
 
0.3%
cl3078
 
0.3%
Other values (11)36
 
1.1%

Most occurring characters

ValueCountFrequency (%)
14770
25.0%
S3075
16.1%
T3065
16.1%
U3065
16.1%
21785
 
9.4%
01384
 
7.3%
31383
 
7.3%
r95
 
0.5%
C62
 
0.3%
o59
 
0.3%
Other values (21)317
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9375
49.2%
Decimal Number9364
49.1%
Lowercase Letter282
 
1.5%
Space Separator39
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r95
33.7%
o59
20.9%
d29
 
10.3%
i27
 
9.6%
c21
 
7.4%
e11
 
3.9%
h10
 
3.5%
p10
 
3.5%
a10
 
3.5%
b6
 
2.1%
Other values (2)4
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
S3075
32.8%
T3065
32.7%
U3065
32.7%
C62
 
0.7%
L48
 
0.5%
A30
 
0.3%
D13
 
0.1%
B7
 
0.1%
E6
 
0.1%
V3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
14770
50.9%
21785
 
19.1%
01384
 
14.8%
31383
 
14.8%
719
 
0.2%
616
 
0.2%
47
 
0.1%
Space Separator
ValueCountFrequency (%)
39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9657
50.7%
Common9403
49.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S3075
31.8%
T3065
31.7%
U3065
31.7%
r95
 
1.0%
C62
 
0.6%
o59
 
0.6%
L48
 
0.5%
A30
 
0.3%
d29
 
0.3%
i27
 
0.3%
Other values (13)102
 
1.1%
Common
ValueCountFrequency (%)
14770
50.7%
21785
 
19.0%
01384
 
14.7%
31383
 
14.7%
39
 
0.4%
719
 
0.2%
616
 
0.2%
47
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII19060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14770
25.0%
S3075
16.1%
T3065
16.1%
U3065
16.1%
21785
 
9.4%
01384
 
7.3%
31383
 
7.3%
r95
 
0.5%
C62
 
0.3%
o59
 
0.3%
Other values (21)317
 
1.7%

Time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3136
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
11:31:01 AM
 
2
12:43:01 PM
 
2
4:20:01 AM
 
2
12:49:17 PM
 
2
8:37:47 AM
 
2
Other values (3131)
3154 

Length

Max length11
Median length10
Mean length10.26548673
Min length10

Characters and Unicode

Total characters32480
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3108 ?
Unique (%)98.2%

Sample

1st row2:12:13 PM
2nd row2:13:16 PM
3rd row2:14:18 PM
4th row2:15:21 PM
5th row2:16:24 PM

Common Values

ValueCountFrequency (%)
11:31:01 AM2
 
0.1%
12:43:01 PM2
 
0.1%
4:20:01 AM2
 
0.1%
12:49:17 PM2
 
0.1%
8:37:47 AM2
 
0.1%
4:13:45 AM2
 
0.1%
11:56:00 PM2
 
0.1%
8:44:03 AM2
 
0.1%
3:26:44 PM2
 
0.1%
7:38:14 PM2
 
0.1%
Other values (3126)3144
99.4%

Length

2022-08-23T12:24:37.261973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm1586
25.1%
am1575
24.9%
11:31:012
 
< 0.1%
10:37:432
 
< 0.1%
11:24:452
 
< 0.1%
11:37:172
 
< 0.1%
4:32:332
 
< 0.1%
7:03:482
 
< 0.1%
10:50:152
 
< 0.1%
6:57:322
 
< 0.1%
Other values (3109)3148
49.8%

Most occurring characters

ValueCountFrequency (%)
:6322
19.5%
3161
9.7%
M3161
9.7%
13110
9.6%
22272
 
7.0%
01941
 
6.0%
31939
 
6.0%
41939
 
6.0%
51926
 
5.9%
P1586
 
4.9%
Other values (6)5123
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16672
51.3%
Other Punctuation6325
 
19.5%
Uppercase Letter6322
 
19.5%
Space Separator3161
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13110
18.7%
22272
13.6%
01941
11.6%
31939
11.6%
41939
11.6%
51926
11.6%
7890
 
5.3%
6888
 
5.3%
8887
 
5.3%
9880
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
M3161
50.0%
P1586
25.1%
A1575
24.9%
Other Punctuation
ValueCountFrequency (%)
:6322
> 99.9%
.3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26158
80.5%
Latin6322
 
19.5%

Most frequent character per script

Common
ValueCountFrequency (%)
:6322
24.2%
3161
12.1%
13110
11.9%
22272
 
8.7%
01941
 
7.4%
31939
 
7.4%
41939
 
7.4%
51926
 
7.4%
7890
 
3.4%
6888
 
3.4%
Other values (3)1770
 
6.8%
Latin
ValueCountFrequency (%)
M3161
50.0%
P1586
25.1%
A1575
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII32480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
:6322
19.5%
3161
9.7%
M3161
9.7%
13110
9.6%
22272
 
7.0%
01941
 
6.0%
31939
 
6.0%
41939
 
6.0%
51926
 
5.9%
P1586
 
4.9%
Other values (6)5123
15.8%

Decimal
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3136
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.0449293
Minimum0.0028
Maximum23.9931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:37.405695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0028
5-th percentile1.272995
Q16.3502
median12.0347
Q317.64205
95-th percentile22.72362
Maximum23.9931
Range23.9903
Interquartile range (IQR)11.29185

Descriptive statistics

Standard deviation6.746535177
Coefficient of variation (CV)0.5601141368
Kurtosis-1.106954365
Mean12.0449293
Median Absolute Deviation (MAD)5.6485
Skewness-0.01996947141
Sum38110.1563
Variance45.5157369
MonotonicityNot monotonic
2022-08-23T12:24:37.573687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.51692
 
0.1%
12.71692
 
0.1%
4.33362
 
0.1%
12.82142
 
0.1%
8.62972
 
0.1%
4.22922
 
0.1%
23.93332
 
0.1%
8.73422
 
0.1%
15.44562
 
0.1%
19.63722
 
0.1%
Other values (3126)3144
99.4%
ValueCountFrequency (%)
0.00281
< 0.1%
0.00781
< 0.1%
0.02031
< 0.1%
0.02281
< 0.1%
0.03751
< 0.1%
0.03781
< 0.1%
0.05251
< 0.1%
0.0551
< 0.1%
0.06751
< 0.1%
0.07221
< 0.1%
ValueCountFrequency (%)
23.99311
< 0.1%
23.98531
< 0.1%
23.97811
< 0.1%
23.96811
< 0.1%
23.96311
< 0.1%
23.95061
< 0.1%
23.94811
< 0.1%
23.93332
0.1%
23.91831
< 0.1%
23.91581
< 0.1%

Illumination level (Lux)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1502
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.8672171
Minimum0
Maximum883.58
Zeros730
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:37.742300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median73.1575
Q3468.205
95-th percentile472.42825
Maximum883.58
Range883.58
Interquartile range (IQR)468.185

Descriptive statistics

Standard deviation224.0101378
Coefficient of variation (CV)1.005128258
Kurtosis-1.89295974
Mean222.8672171
Median Absolute Deviation (MAD)73.1575
Skewness0.1437223286
Sum705151.875
Variance50180.54182
MonotonicityNot monotonic
2022-08-23T12:24:37.902120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0730
 
23.1%
0.0249
 
1.5%
0.1231
 
1.0%
0.0128
 
0.9%
0.0617
 
0.5%
0.6914
 
0.4%
468.1913
 
0.4%
0.0912
 
0.4%
0.4112
 
0.4%
469.04510
 
0.3%
Other values (1492)2248
71.0%
ValueCountFrequency (%)
0730
23.1%
0.0128
 
0.9%
0.0249
 
1.5%
0.032
 
0.1%
0.056
 
0.2%
0.0552
 
0.1%
0.0617
 
0.5%
0.072
 
0.1%
0.0912
 
0.4%
0.13
 
0.1%
ValueCountFrequency (%)
883.581
< 0.1%
666.261
< 0.1%
625.1551
< 0.1%
622.091
< 0.1%
616.591
< 0.1%
616.041
< 0.1%
593.381
< 0.1%
563.3651
< 0.1%
513.2951
< 0.1%
5121
< 0.1%

Color Tempreture
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct871
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5936.109039
Minimum3777
Maximum8612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:38.061842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3777
5-th percentile3910
Q14012
median7016
Q37300
95-th percentile7438.85
Maximum8612
Range4835
Interquartile range (IQR)3288

Descriptive statistics

Standard deviation1546.860785
Coefficient of variation (CV)0.2605849681
Kurtosis-1.789488911
Mean5936.109039
Median Absolute Deviation (MAD)320
Skewness-0.3422912875
Sum18781849
Variance2392778.288
MonotonicityNot monotonic
2022-08-23T12:24:38.226498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3910227
 
7.2%
7311191
 
6.0%
3991164
 
5.2%
3996146
 
4.6%
7309130
 
4.1%
729896
 
3.0%
397064
 
2.0%
393146
 
1.5%
730242
 
1.3%
397440
 
1.3%
Other values (861)2018
63.8%
ValueCountFrequency (%)
37771
 
< 0.1%
38531
 
< 0.1%
38552
 
0.1%
38661
 
< 0.1%
38761
 
< 0.1%
38801
 
< 0.1%
38931
 
< 0.1%
38972
 
0.1%
3910227
7.2%
39121
 
< 0.1%
ValueCountFrequency (%)
86121
< 0.1%
83831
< 0.1%
82301
< 0.1%
81241
< 0.1%
81231
< 0.1%
81181
< 0.1%
81131
< 0.1%
81021
< 0.1%
79881
< 0.1%
79591
< 0.1%

Average Noise level
Real number (ℝ≥0)

Distinct1357
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.31498704
Minimum16.8667
Maximum114.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:38.390824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.8667
5-th percentile18.7833
Q120.8
median25.6
Q337.5625
95-th percentile70.045005
Maximum114.9
Range98.0333
Interquartile range (IQR)16.7625

Descriptive statistics

Standard deviation16.82750113
Coefficient of variation (CV)0.5207336494
Kurtosis3.886573837
Mean32.31498704
Median Absolute Deviation (MAD)5.5333
Skewness1.955398087
Sum102244.619
Variance283.1647943
MonotonicityNot monotonic
2022-08-23T12:24:38.551647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.725
 
0.8%
24.925
 
0.8%
25.522
 
0.7%
25.922
 
0.7%
25.321
 
0.7%
25.820
 
0.6%
25.620
 
0.6%
25.419
 
0.6%
2519
 
0.6%
2619
 
0.6%
Other values (1347)2952
93.3%
ValueCountFrequency (%)
16.86672
0.1%
171
 
< 0.1%
17.01671
 
< 0.1%
17.051
 
< 0.1%
17.21
 
< 0.1%
17.351
 
< 0.1%
17.38332
0.1%
17.41
 
< 0.1%
17.453
0.1%
17.48331
 
< 0.1%
ValueCountFrequency (%)
114.91
 
< 0.1%
114.26671
 
< 0.1%
111.78331
 
< 0.1%
111.21671
 
< 0.1%
110.851
 
< 0.1%
109.56671
 
< 0.1%
109.26671
 
< 0.1%
108.33333
0.1%
106.41671
 
< 0.1%
106.21671
 
< 0.1%

O2 Concentration (%Vol)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct75
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.32249052
Minimum20
Maximum20.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:38.718241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20.16
Q120.21
median20.31
Q320.42
95-th percentile20.52
Maximum20.88
Range0.88
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1240085241
Coefficient of variation (CV)0.006102033801
Kurtosis-0.1859759027
Mean20.32249052
Median Absolute Deviation (MAD)0.1
Skewness0.487967663
Sum64300.36
Variance0.01537811404
MonotonicityNot monotonic
2022-08-23T12:24:38.880733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.21169
 
5.3%
20.2162
 
5.1%
20.22141
 
4.5%
20.19137
 
4.3%
20.4135
 
4.3%
20.38135
 
4.3%
20.42135
 
4.3%
20.23134
 
4.2%
20.25123
 
3.9%
20.44117
 
3.7%
Other values (65)1776
56.1%
ValueCountFrequency (%)
201
 
< 0.1%
20.011
 
< 0.1%
20.051
 
< 0.1%
20.063
 
0.1%
20.072
 
0.1%
20.094
 
0.1%
20.15
 
0.2%
20.117
0.2%
20.1211
0.3%
20.1316
0.5%
ValueCountFrequency (%)
20.881
 
< 0.1%
20.81
 
< 0.1%
20.791
 
< 0.1%
20.771
 
< 0.1%
20.762
0.1%
20.752
0.1%
20.744
0.1%
20.733
0.1%
20.721
 
< 0.1%
20.711
 
< 0.1%

CO2 at any moment (PPM)
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3163
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
684.9626
 
2
1110
 
1
648.7088
 
1
641.6765
 
1
640.1418
 
1
Other values (3158)
3158 

Length

Max length9
Median length8
Mean length8.015486726
Min length1

Characters and Unicode

Total characters25361
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3162 ?
Unique (%)99.9%

Sample

1st row1110
2nd row1099.5644
3rd row1102.5324
4th row1074.0251
5th row1129.104

Common Values

ValueCountFrequency (%)
684.96262
 
0.1%
11101
 
< 0.1%
648.70881
 
< 0.1%
641.67651
 
< 0.1%
640.14181
 
< 0.1%
696.43631
 
< 0.1%
645.64821
 
< 0.1%
653.7511
 
< 0.1%
628.77311
 
< 0.1%
655.9361
 
< 0.1%
Other values (3153)3153
99.7%

Length

2022-08-23T12:24:39.035154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
684.96262
 
0.1%
1068.51591
 
< 0.1%
1116.62061
 
< 0.1%
1102.53241
 
< 0.1%
1074.02511
 
< 0.1%
1129.1041
 
< 0.1%
1178.7321
 
< 0.1%
1109.32671
 
< 0.1%
1161.54231
 
< 0.1%
1169.55881
 
< 0.1%
Other values (3153)3153
99.7%

Most occurring characters

ValueCountFrequency (%)
.3162
12.5%
72484
9.8%
62472
9.7%
12459
9.7%
42363
9.3%
52242
8.8%
82221
8.8%
92208
8.7%
32029
8.0%
21949
7.7%
Other values (2)1772
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22198
87.5%
Other Punctuation3162
 
12.5%
Modifier Symbol1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
72484
11.2%
62472
11.1%
12459
11.1%
42363
10.6%
52242
10.1%
82221
10.0%
92208
9.9%
32029
9.1%
21949
8.8%
01771
8.0%
Other Punctuation
ValueCountFrequency (%)
.3162
100.0%
Modifier Symbol
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25361
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3162
12.5%
72484
9.8%
62472
9.7%
12459
9.7%
42363
9.3%
52242
8.8%
82221
8.8%
92208
8.7%
32029
8.0%
21949
7.7%
Other values (2)1772
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3162
12.5%
72484
9.8%
62472
9.7%
12459
9.7%
42363
9.3%
52242
8.8%
82221
8.8%
92208
8.7%
32029
8.0%
21949
7.7%
Other values (2)1772
7.0%

LPG concen (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct367
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.034980247
Minimum2.077
Maximum5.7775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:39.189748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.077
5-th percentile2.4544
Q12.8347
median2.948
Q33.1236
95-th percentile3.9071
Maximum5.7775
Range3.7005
Interquartile range (IQR)0.2889

Descriptive statistics

Standard deviation0.4170809188
Coefficient of variation (CV)0.1374245909
Kurtosis5.079904205
Mean3.034980247
Median Absolute Deviation (MAD)0.1292
Skewness1.64107229
Sum9602.6775
Variance0.1739564928
MonotonicityNot monotonic
2022-08-23T12:24:39.355731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.980976
 
2.4%
2.94874
 
2.3%
2.915372
 
2.3%
2.964468
 
2.1%
2.931665
 
2.1%
2.953665
 
2.1%
2.997560
 
1.9%
2.834759
 
1.9%
2.899158
 
1.8%
2.802957
 
1.8%
Other values (357)2510
79.3%
ValueCountFrequency (%)
2.0771
 
< 0.1%
2.08291
 
< 0.1%
2.10663
0.1%
2.13051
 
< 0.1%
2.20961
 
< 0.1%
2.21641
 
< 0.1%
2.231
 
< 0.1%
2.24364
0.1%
2.24681
 
< 0.1%
2.25731
 
< 0.1%
ValueCountFrequency (%)
5.77751
 
< 0.1%
5.75131
 
< 0.1%
5.72521
 
< 0.1%
5.57053
0.1%
5.3691
 
< 0.1%
5.14862
0.1%
5.07661
 
< 0.1%
5.06841
 
< 0.1%
5.05271
 
< 0.1%
4.91141
 
< 0.1%

Alcohole Concen (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002605404551
Minimum0.0014
Maximum0.006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:39.524704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014
5-th percentile0.0018
Q10.002
median0.0026
Q30.0028
95-th percentile0.004
Maximum0.006
Range0.0046
Interquartile range (IQR)0.0008

Descriptive statistics

Standard deviation0.0006833037924
Coefficient of variation (CV)0.2622639897
Kurtosis2.177205839
Mean0.002605404551
Median Absolute Deviation (MAD)0.0004
Skewness1.232819061
Sum8.2435
Variance4.669040728 × 10-7
MonotonicityNot monotonic
2022-08-23T12:24:39.685575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0026372
11.8%
0.0019318
 
10.1%
0.002277
 
8.8%
0.0027265
 
8.4%
0.0028258
 
8.2%
0.0024240
 
7.6%
0.0025202
 
6.4%
0.0018138
 
4.4%
0.0021104
 
3.3%
0.002392
 
2.9%
Other values (35)898
28.4%
ValueCountFrequency (%)
0.00143
 
0.1%
0.001524
 
0.8%
0.001634
 
1.1%
0.001747
 
1.5%
0.0018138
4.4%
0.0019318
10.1%
0.002277
8.8%
0.0021104
 
3.3%
0.002260
 
1.9%
0.002392
 
2.9%
ValueCountFrequency (%)
0.0061
 
< 0.1%
0.00591
 
< 0.1%
0.00584
0.1%
0.00571
 
< 0.1%
0.00562
0.1%
0.00553
0.1%
0.00543
0.1%
0.00514
0.1%
0.0052
0.1%
0.00494
0.1%

Methan Concen (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct273
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2735941214
Minimum0.1762
Maximum1.3546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:39.846509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1762
5-th percentile0.2107
Q10.2365
median0.2435
Q30.2996
95-th percentile0.397
Maximum1.3546
Range1.1784
Interquartile range (IQR)0.0631

Descriptive statistics

Standard deviation0.07654938669
Coefficient of variation (CV)0.2797917817
Kurtosis46.43339191
Mean0.2735941214
Median Absolute Deviation (MAD)0.0169
Skewness4.978502546
Sum865.6518
Variance0.005859808603
MonotonicityNot monotonic
2022-08-23T12:24:40.021305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2365234
 
7.4%
0.2398185
 
5.8%
0.2332145
 
4.6%
0.2299107
 
3.4%
0.243594
 
3.0%
0.243291
 
2.9%
0.226688
 
2.8%
0.245687
 
2.7%
0.241585
 
2.7%
0.223464
 
2.0%
Other values (263)1984
62.7%
ValueCountFrequency (%)
0.17621
 
< 0.1%
0.17741
 
< 0.1%
0.18341
 
< 0.1%
0.18821
 
< 0.1%
0.19072
 
0.1%
0.19251
 
< 0.1%
0.19261
 
< 0.1%
0.19436
0.2%
0.19441
 
< 0.1%
0.19553
0.1%
ValueCountFrequency (%)
1.35461
< 0.1%
1.30271
< 0.1%
1.12321
< 0.1%
1.09751
< 0.1%
1.05222
0.1%
1.03741
< 0.1%
0.91431
< 0.1%
0.8221
< 0.1%
0.80931
< 0.1%
0.76791
< 0.1%

CO concen (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct251
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.505731637
Minimum3.7507
Maximum8.0523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:40.194619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.7507
5-th percentile4.20447
Q14.481
median5.6481
Q36.0619
95-th percentile7.2264
Maximum8.0523
Range4.3016
Interquartile range (IQR)1.5809

Descriptive statistics

Standard deviation0.9391484547
Coefficient of variation (CV)0.170576504
Kurtosis-0.811611637
Mean5.505731637
Median Absolute Deviation (MAD)0.651
Skewness0.1665460902
Sum17420.1349
Variance0.8819998199
MonotonicityNot monotonic
2022-08-23T12:24:40.354630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0153122
 
3.9%
6.0619115
 
3.6%
5.968894
 
3.0%
4.281486
 
2.7%
5.73983
 
2.6%
6.108682
 
2.6%
4.301274
 
2.3%
4.340969
 
2.2%
4.261768
 
2.1%
5.784667
 
2.1%
Other values (241)2304
72.8%
ValueCountFrequency (%)
3.75072
0.1%
3.80733
0.1%
3.82161
 
< 0.1%
3.83581
 
< 0.1%
3.85011
 
< 0.1%
3.91311
 
< 0.1%
3.92171
 
< 0.1%
3.97031
 
< 0.1%
4.00863
0.1%
4.02792
0.1%
ValueCountFrequency (%)
8.05231
 
< 0.1%
7.84211
 
< 0.1%
7.78993
 
0.1%
7.73792
 
0.1%
7.69591
 
< 0.1%
7.68611
 
< 0.1%
7.63446
 
0.2%
7.58285
 
0.2%
7.531512
0.4%
7.480221
0.7%

Hydrogyn Concen (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct250
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.77125351
Minimum39.1781
Maximum57.8762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:40.520838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum39.1781
5-th percentile42.0398
Q145.0667
median45.8444
Q351.7569
95-th percentile53.344
Maximum57.8762
Range18.6981
Interquartile range (IQR)6.6902

Descriptive statistics

Standard deviation3.846223369
Coefficient of variation (CV)0.08051334405
Kurtosis-1.347841108
Mean47.77125351
Median Absolute Deviation (MAD)2.4347
Skewness0.1956973145
Sum151148.2461
Variance14.79343421
MonotonicityNot monotonic
2022-08-23T12:24:40.681967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.7473133
 
4.2%
45.6501128
 
4.0%
45.8444107
 
3.4%
45.552997
 
3.1%
45.455784
 
2.7%
45.261375
 
2.4%
45.066773
 
2.3%
44.872169
 
2.2%
45.16465
 
2.1%
44.969462
 
2.0%
Other values (240)2271
71.8%
ValueCountFrequency (%)
39.17811
 
< 0.1%
39.77261
 
< 0.1%
39.94231
 
< 0.1%
40.02711
 
< 0.1%
40.11193
0.1%
40.19671
 
< 0.1%
40.28145
0.2%
40.35871
 
< 0.1%
40.44441
 
< 0.1%
40.4511
 
< 0.1%
ValueCountFrequency (%)
57.87621
 
< 0.1%
57.67081
 
< 0.1%
56.64281
 
< 0.1%
55.69111
 
< 0.1%
55.33893
0.1%
55.27022
0.1%
55.20153
0.1%
55.13281
 
< 0.1%
55.0642
0.1%
54.99531
 
< 0.1%

Netric tVOC level (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct148
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4175506321
Minimum0.0952
Maximum0.9767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:40.856076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0952
5-th percentile0.2061
Q10.2631
median0.4003
Q30.55
95-th percentile0.7136
Maximum0.9767
Range0.8815
Interquartile range (IQR)0.2869

Descriptive statistics

Standard deviation0.1692024058
Coefficient of variation (CV)0.4052260799
Kurtosis-0.6944731034
Mean0.4175506321
Median Absolute Deviation (MAD)0.1409
Skewness0.462348098
Sum1321.1302
Variance0.02862945413
MonotonicityNot monotonic
2022-08-23T12:24:41.020174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.55126
 
4.0%
0.5714118
 
3.7%
0.5292116
 
3.7%
0.5934114
 
3.6%
0.6161108
 
3.4%
0.4895107
 
3.4%
0.4705102
 
3.2%
0.5091102
 
3.2%
0.452284
 
2.7%
0.41783
 
2.6%
Other values (138)2104
66.5%
ValueCountFrequency (%)
0.09521
 
< 0.1%
0.09751
 
< 0.1%
0.10741
 
< 0.1%
0.10991
 
< 0.1%
0.11263
0.1%
0.11521
 
< 0.1%
0.11791
 
< 0.1%
0.12071
 
< 0.1%
0.12351
 
< 0.1%
0.12641
 
< 0.1%
ValueCountFrequency (%)
0.97673
 
0.1%
0.94433
 
0.1%
0.91272
 
0.1%
0.881910
 
0.3%
0.851911
 
0.3%
0.822710
 
0.3%
0.794316
 
0.5%
0.766733
1.0%
0.739834
1.1%
0.713654
1.7%

VOC Formerdahied Level (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct164
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5297552149
Minimum0.0594
Maximum5.2826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:41.186621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0594
5-th percentile0.2507
Q10.3325
median0.3784
Q30.5108
95-th percentile0.9877
Maximum5.2826
Range5.2232
Interquartile range (IQR)0.1783

Descriptive statistics

Standard deviation0.6236659967
Coefficient of variation (CV)1.177272029
Kurtosis32.99993445
Mean0.5297552149
Median Absolute Deviation (MAD)0.0732
Skewness5.546928065
Sum1676.1455
Variance0.3889592754
MonotonicityNot monotonic
2022-08-23T12:24:41.354695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3052395
 
12.5%
0.3351287
 
9.1%
0.4028205
 
6.5%
0.3784205
 
6.5%
0.3668184
 
5.8%
0.4005132
 
4.2%
0.3549122
 
3.9%
0.4283114
 
3.6%
0.5134112
 
3.5%
0.277196
 
3.0%
Other values (154)1312
41.5%
ValueCountFrequency (%)
0.05941
 
< 0.1%
0.06252
0.1%
0.06571
 
< 0.1%
0.0691
 
< 0.1%
0.07242
0.1%
0.07591
 
< 0.1%
0.07952
0.1%
0.0873
0.1%
0.0911
 
< 0.1%
0.0951
 
< 0.1%
ValueCountFrequency (%)
5.28262
 
0.1%
5.14885
 
0.2%
5.01738
0.3%
4.88813
0.4%
4.7612
 
0.1%
4.63623
 
0.1%
4.51352
 
0.1%
4.3933
 
0.1%
4.27472
 
0.1%
4.15841
 
< 0.1%

Different VOC (CO) Level (ppm)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.802040582
Minimum1.7365
Maximum9.2512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:41.524943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.7365
5-th percentile2.4563
Q13.2148
median3.6894
Q34.0057
95-th percentile6.5349
Maximum9.2512
Range7.5147
Interquartile range (IQR)0.7909

Descriptive statistics

Standard deviation1.036033411
Coefficient of variation (CV)0.2724940433
Kurtosis3.205925315
Mean3.802040582
Median Absolute Deviation (MAD)0.3205
Skewness1.517424609
Sum12029.6564
Variance1.073365228
MonotonicityNot monotonic
2022-08-23T12:24:41.677960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4.0057586
18.5%
3.2148461
14.6%
3.6894374
11.8%
3.3736320
10.1%
4.0099269
8.5%
2.4563201
 
6.4%
3.0629196
 
6.2%
4.8246154
 
4.9%
5.6685120
 
3.8%
6.5349120
 
3.8%
Other values (29)363
11.5%
ValueCountFrequency (%)
1.736527
 
0.9%
1.88082
 
0.1%
1.97272
 
0.1%
2.02375
 
0.2%
2.16463
 
0.1%
2.16844
 
0.1%
2.31481
 
< 0.1%
2.4563201
6.4%
2.45829
 
0.9%
2.46298
 
0.3%
ValueCountFrequency (%)
9.25121
 
< 0.1%
8.327712
 
0.4%
7.421838
 
1.2%
6.5349120
3.8%
6.25362
 
0.1%
5.67673
 
0.1%
5.6685120
3.8%
5.1871
 
< 0.1%
5.119
 
0.3%
4.8246154
4.9%

Room Temp ( C )
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1387
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.3789957
Minimum21.4
Maximum27.3476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:41.846295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21.4
5-th percentile22.15
Q122.575
median22.925
Q324.275
95-th percentile25.12597
Maximum27.3476
Range5.9476
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.009821165
Coefficient of variation (CV)0.04319352197
Kurtosis-0.8537874914
Mean23.3789957
Median Absolute Deviation (MAD)0.65
Skewness0.5018293229
Sum73971.1424
Variance1.019738784
MonotonicityNot monotonic
2022-08-23T12:24:42.006734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.75157
 
5.0%
22.7113
 
3.6%
22.5593
 
2.9%
22.883
 
2.6%
22.672
 
2.3%
22.4564
 
2.0%
22.561
 
1.9%
22.77560
 
1.9%
22.6558
 
1.8%
22.355
 
1.7%
Other values (1377)2348
74.2%
ValueCountFrequency (%)
21.41
 
< 0.1%
21.53
 
0.1%
21.5254
 
0.1%
21.552
 
0.1%
21.62
 
0.1%
21.6251
 
< 0.1%
21.651
 
< 0.1%
21.74
 
0.1%
21.7252
 
0.1%
21.7511
0.3%
ValueCountFrequency (%)
27.34761
< 0.1%
26.8021
< 0.1%
26.69851
< 0.1%
26.43871
< 0.1%
26.40271
< 0.1%
26.07621
< 0.1%
26.0751
< 0.1%
26.051
< 0.1%
26.01121
< 0.1%
261
< 0.1%

Room reative humadity %
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct330
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.73085335
Minimum22.05
Maximum71.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:42.176288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22.05
5-th percentile51.4
Q154.3
median55.45
Q360
95-th percentile62.9425
Maximum71.2
Range49.15
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation3.800967625
Coefficient of variation (CV)0.06700000793
Kurtosis1.63313078
Mean56.73085335
Median Absolute Deviation (MAD)2.3
Skewness-0.04469925071
Sum179496.42
Variance14.44735488
MonotonicityNot monotonic
2022-08-23T12:24:42.346978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.65172
 
5.4%
54.45168
 
5.3%
54.1143
 
4.5%
54.55142
 
4.5%
54.3107
 
3.4%
54.3577
 
2.4%
54.259
 
1.9%
54.2551
 
1.6%
60.348
 
1.5%
52.8539
 
1.2%
Other values (320)2158
68.2%
ValueCountFrequency (%)
22.051
 
< 0.1%
46.551
 
< 0.1%
46.61
 
< 0.1%
46.81
 
< 0.1%
47.051
 
< 0.1%
47.251
 
< 0.1%
47.41
 
< 0.1%
47.51
 
< 0.1%
47.958
0.3%
487
0.2%
ValueCountFrequency (%)
71.21
 
< 0.1%
70.21
 
< 0.1%
69.451
 
< 0.1%
69.41
 
< 0.1%
65.71
 
< 0.1%
65.451
 
< 0.1%
65.351
 
< 0.1%
65.31
 
< 0.1%
65.252
0.1%
65.153
0.1%

Radiant Temp 1 (C)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.22850822
Minimum14
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:42.486075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile18
Q120
median21
Q322
95-th percentile24
Maximum28
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.854985213
Coefficient of variation (CV)0.08738179783
Kurtosis0.2746991497
Mean21.22850822
Median Absolute Deviation (MAD)1
Skewness-0.05897243061
Sum67167
Variance3.440970142
MonotonicityNot monotonic
2022-08-23T12:24:42.600618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22662
20.9%
21648
20.5%
20513
16.2%
23462
14.6%
19350
11.1%
24223
 
7.0%
18157
 
5.0%
2555
 
1.7%
1737
 
1.2%
2623
 
0.7%
Other values (5)34
 
1.1%
ValueCountFrequency (%)
143
 
0.1%
155
 
0.2%
1613
 
0.4%
1737
 
1.2%
18157
 
5.0%
19350
11.1%
20513
16.2%
21648
20.5%
22662
20.9%
23462
14.6%
ValueCountFrequency (%)
283
 
0.1%
2710
 
0.3%
2623
 
0.7%
2555
 
1.7%
24223
 
7.0%
23462
14.6%
22662
20.9%
21648
20.5%
20513
16.2%
19350
11.1%

Radiant Temp 2 (C)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.65960809
Minimum15
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:42.730896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20
Q121
median23
Q324
95-th percentile25
Maximum31
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.829985074
Coefficient of variation (CV)0.08075978486
Kurtosis0.456914296
Mean22.65960809
Median Absolute Deviation (MAD)1
Skewness0.05082100064
Sum71695
Variance3.348845373
MonotonicityNot monotonic
2022-08-23T12:24:42.851788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
22654
20.7%
23630
19.9%
24554
17.5%
21517
16.3%
25322
10.2%
20220
 
7.0%
2698
 
3.1%
1974
 
2.3%
2741
 
1.3%
1822
 
0.7%
Other values (7)32
 
1.0%
ValueCountFrequency (%)
151
 
< 0.1%
164
 
0.1%
178
 
0.3%
1822
 
0.7%
1974
 
2.3%
20220
 
7.0%
21517
16.3%
22654
20.7%
23630
19.9%
24554
17.5%
ValueCountFrequency (%)
311
 
< 0.1%
302
 
0.1%
293
 
0.1%
2813
 
0.4%
2741
 
1.3%
2698
 
3.1%
25322
10.2%
24554
17.5%
23630
19.9%
22654
20.7%

Radiant Temp 3 (C)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.51580278
Minimum17
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:42.979220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile21
Q122
median24
Q325
95-th percentile26
Maximum32
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.865388516
Coefficient of variation (CV)0.07932489201
Kurtosis1.085810415
Mean23.51580278
Median Absolute Deviation (MAD)1
Skewness0.2702375399
Sum74404
Variance3.479674316
MonotonicityNot monotonic
2022-08-23T12:24:43.098319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
24730
23.1%
23659
20.8%
22526
16.6%
25487
15.4%
26233
 
7.4%
21226
 
7.1%
20116
 
3.7%
2774
 
2.3%
2839
 
1.2%
1924
 
0.8%
Other values (6)50
 
1.6%
ValueCountFrequency (%)
173
 
0.1%
1812
 
0.4%
1924
 
0.8%
20116
 
3.7%
21226
 
7.1%
22526
16.6%
23659
20.8%
24730
23.1%
25487
15.4%
26233
 
7.4%
ValueCountFrequency (%)
322
 
0.1%
316
 
0.2%
3010
 
0.3%
2917
 
0.5%
2839
 
1.2%
2774
 
2.3%
26233
 
7.4%
25487
15.4%
24730
23.1%
23659
20.8%

Radiant Temp 1 (C).1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct37
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.46797364
Minimum16.3333
Maximum29.6667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:43.243445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.3333
5-th percentile20
Q121.3333
median22.3333
Q323.3333
95-th percentile25
Maximum29.6667
Range13.3334
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.494239681
Coefficient of variation (CV)0.06650531572
Kurtosis0.4682373354
Mean22.46797364
Median Absolute Deviation (MAD)1
Skewness0.1817617954
Sum71088.6686
Variance2.232752223
MonotonicityNot monotonic
2022-08-23T12:24:43.403073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
21.6667279
 
8.8%
23266
 
8.4%
22.6667259
 
8.2%
23.3333250
 
7.9%
22247
 
7.8%
22.3333241
 
7.6%
21.3333225
 
7.1%
23.6667214
 
6.8%
21211
 
6.7%
24179
 
5.7%
Other values (27)793
25.1%
ValueCountFrequency (%)
16.33331
 
< 0.1%
171
 
< 0.1%
17.33331
 
< 0.1%
17.66673
 
0.1%
184
 
0.1%
18.33333
 
0.1%
18.66675
 
0.2%
199
 
0.3%
19.333321
0.7%
19.666747
1.5%
ValueCountFrequency (%)
29.66671
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
27.66674
 
0.1%
27.33332
 
0.1%
278
 
0.3%
26.666712
0.4%
26.333310
0.3%
2614
0.4%
25.666720
0.6%

Wind Speed (mm/s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct35
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.2882427
Minimum74
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2022-08-23T12:24:43.562041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile126.15
Q1133
median135
Q3136
95-th percentile140
Maximum160
Range86
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.235672046
Coefficient of variation (CV)0.03154164475
Kurtosis14.80655202
Mean134.2882427
Median Absolute Deviation (MAD)2
Skewness-0.7080821837
Sum424888
Variance17.94091768
MonotonicityNot monotonic
2022-08-23T12:24:43.703587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
135659
20.8%
136499
15.8%
134372
11.8%
133201
 
6.4%
137186
 
5.9%
138142
 
4.5%
132138
 
4.4%
139108
 
3.4%
130107
 
3.4%
140103
 
3.3%
Other values (25)649
20.5%
ValueCountFrequency (%)
741
 
< 0.1%
1181
 
< 0.1%
1241
 
< 0.1%
12582
2.6%
12674
2.3%
12784
2.7%
12891
2.9%
129101
3.2%
130107
3.4%
131102
3.2%
ValueCountFrequency (%)
1601
 
< 0.1%
1551
 
< 0.1%
1542
 
0.1%
1533
 
0.1%
1522
 
0.1%
1515
0.2%
1504
0.1%
1494
0.1%
1481
 
< 0.1%
1479
0.3%

Interactions

2022-08-23T12:24:33.593509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:40.764025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.679114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:46.305487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:48.905870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:51.501623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:54.133964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:56.886015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:00.290917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.995332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:05.578263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:08.353367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.973049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:13.633637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:16.399092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:19.058551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:22.619844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:25.355531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:28.098871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.843157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:33.719172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:41.187173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.804818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:46.430367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:49.031016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:51.629681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:54.268143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:57.014957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:00.422004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:03.119853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:05.712074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:08.481903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:11.102291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:13.770426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:16.528662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:19.189748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:22.752286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:25.490075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:28.231072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.987616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:33.844088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:41.310524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.929783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:46.554511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:49.153951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:51.754714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:54.399292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:57.139062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:00.550094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:03.243215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:05.844885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-23T12:24:12.542237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:15.274510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:17.965465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:21.529179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:24.239079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:26.978528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:29.724432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:32.476698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:35.275954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:42.752103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:45.374693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:47.980082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:50.577924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:53.194015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:55.907966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:59.320843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.032509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:04.659683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:07.368137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.042050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:12.681146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:15.418252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:18.106059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:21.669867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:24.383291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:27.123335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:29.867019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:32.621246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:35.406848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:42.882783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:45.507988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:48.107883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:50.705569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:53.325855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:56.043622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:59.450085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.167869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:04.787110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:07.504611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.171746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:12.812976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:15.552881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:18.239137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:21.801842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:24.518663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:27.257829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.002346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:32.756763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:35.545195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.011419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:45.636959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:48.235808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:50.834785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:53.457656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:56.180406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:59.580504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.300975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:04.913381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:07.643164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.301186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:12.945383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:15.689307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:18.372417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:21.933821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:24.654811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:27.396715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.137925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:32.891176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:35.681688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.146635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:45.771974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:48.373365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:50.970778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:53.595564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:56.322571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:59.732803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.442959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:05.048558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:07.787077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.436638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:13.082835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:15.833059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:18.511459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:22.073611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:24.795476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:27.539500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.279635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:33.033368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:35.818355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.282518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:45.907831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:48.509834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:51.105059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:53.732537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:56.467194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:59.887539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.583724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:05.184773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:07.931226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.574014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:13.221771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:15.979231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:18.650692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:22.212328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:24.940335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:27.682655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.424113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:33.176965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:35.953346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.418928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:46.045625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:48.644524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:51.242120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:53.871219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:56.611465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:00.024404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.725379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:05.319447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:08.075322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.711508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:13.360816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:16.122805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:18.790217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:22.350628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:25.082507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:27.824076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.567271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:33.318228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:36.090379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:43.553231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:46.181411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:48.781096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:51.378886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:54.008272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:23:56.754181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:00.164531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:02.865253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:05.454094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:08.222063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:10.847411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:13.504141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:16.267489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:18.930773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:22.490849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:25.224701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:27.968770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:30.711009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-23T12:24:33.462543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-23T12:24:43.888234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-23T12:24:44.172470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-23T12:24:44.455517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-23T12:24:44.737236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-23T12:24:36.323339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-23T12:24:36.874715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

LocationTimeDecimalIllumination level (Lux)Color TempretureAverage Noise levelO2 Concentration (%Vol)CO2 at any moment (PPM)LPG concen (ppm)Alcohole Concen (ppm)Methan Concen (ppm)CO concen (ppm)Hydrogyn Concen (ppm)Netric tVOC level (ppm)VOC Formerdahied Level (ppm)Different VOC (CO) Level (ppm)Room Temp ( C )Room reative humadity %Radiant Temp 1 (C)Radiant Temp 2 (C)Radiant Temp 3 (C)Radiant Temp 1 (C).1Wind Speed (mm/s)
0STU3012:12:13 PM14.2036437.580729224.4020.5711104.85910.00480.48015.721455.69110.18740.46805.187024.721354.4524242123.0000140
1STU3012:13:16 PM14.2211437.965732424.5020.591099.56444.68420.00440.41715.584654.67210.16850.37004.681624.379154.4021262423.6667129
2STU3012:14:18 PM14.2383440.945731230.8020.591102.53244.38980.00400.38575.399253.99290.20780.36004.515223.873554.4022242323.0000134
3STU3012:15:21 PM14.2558437.500731938.5520.561074.02514.24800.00370.34515.282853.65330.18350.31284.022724.247854.4022242523.6667135
4STU3012:16:24 PM14.2733437.945734030.9020.531129.1043.96550.00350.33635.167653.08730.16490.26994.022725.091654.4024272625.6667125
5STU3012:17:26 PM14.2906443.700728564.7520.591178.7323.79740.00320.32255.037452.06840.16140.26183.382823.890054.2020242222.0000129
6STU3012:18:29 PM14.3081441.015730446.8520.511109.32673.69740.00300.30734.940851.44560.15800.20962.916424.241054.2023252424.0000127
7STU3012:19:31 PM14.3253440.060731037.2020.541161.54233.46030.00280.29914.813450.65270.17220.18952.763725.078554.2024242524.3333125
8STU3012:20:34 PM14.3428470.385728830.5520.571169.55883.35870.00270.28784.797550.08620.17960.17082.763723.525254.2022232423.0000128
9STU3012:21:37 PM14.3603439.885731924.0520.591148.54883.26750.00260.28474.750249.91610.14790.17082.612524.165354.1022242523.6667128

Last rows

LocationTimeDecimalIllumination level (Lux)Color TempretureAverage Noise levelO2 Concentration (%Vol)CO2 at any moment (PPM)LPG concen (ppm)Alcohole Concen (ppm)Methan Concen (ppm)CO concen (ppm)Hydrogyn Concen (ppm)Netric tVOC level (ppm)VOC Formerdahied Level (ppm)Different VOC (CO) Level (ppm)Room Temp ( C )Room reative humadity %Radiant Temp 1 (C)Radiant Temp 2 (C)Radiant Temp 3 (C)Radiant Temp 1 (C).1Wind Speed (mm/s)
3154STU2112:42:03 PM14.7008498.035711478.166720.311016.02132.61720.00270.27486.926944.96940.55000.40055.668522.15062.1020232522.6667135
3155STU2112:42:57 PM14.7158499.490711820.583320.351071.13972.60210.00260.26406.877645.06670.82270.47375.668522.15062.1019242522.6667135
3156STU2112:43:51 PM14.7308504.5407131114.266720.321010.73472.63230.00280.27486.926944.67740.55000.43616.534922.65062.2021242523.3333139
3157STU2112:44:45 PM14.7458498.865712822.333320.371030.92572.63230.00260.25706.779345.06670.63940.40055.668522.15062.2523202322.0000137
3158STU2112:45:38 PM14.760664.515713850.250020.321035.73732.63230.00260.28967.176145.06670.66350.47376.534922.15062.0024212623.6667138
3159STU2112:46:33 PM14.775848.840705722.483320.391047.97762.80290.00270.27857.226446.13580.59340.47376.534922.15061.9522202422.0000139
3160STU2112:47:26 PM14.790662.395705531.333320.291045.26232.57210.00260.28597.226445.16400.73980.43615.668522.15062.3025201921.3333136
3161STU2112:48:19 PM14.805351.310703822.850020.381043.71262.72450.00260.27127.276945.84440.61610.51345.668522.12561.9523212121.6667135
3162STU2112:49:13 PM14.820350.350697921.500020.45976.98132.63230.00260.28597.226445.16400.73980.47376.534922.12562.1022232322.6667138
3163STU2112:50:07 PM14.835340.390702420.183320.311036.69292.72450.00260.27487.076045.74730.55000.51345.668522.37562.2520252623.6667136